INVESTIGATION OF C1Q REGULATION OF MONOCYTE DEVELOPMENT

Ph.DDOCTOR OF PHILOSOPH

Method and Apparatus for Triboelectric-Centrifugal Separation

A method for separating two species of particles present in raw feedstock includes electrically charging at least one of the species of particles being separated. Next is the subjecting of the particles to both centrifugal and electrostatic forces. This is done in a manner so as to provide an additive effect and thereby achieve enhanced separation of the two particle species. An apparatus for processing the raw feedstock in accordance with the method is also disclosed

Electrostatic Particle Separation System, Apparatus, and Related Method

An electrostatic separation apparatus or system is provided for separating a particle mixture into two constituent species. The system includes a distributor for differentially tribocharging the particle species forming the mixture and supplying the charged mixture to a plurality of electrostatic separation cells. Each cell includes at least one separator having an inlet, a separation chamber having an electric field zone for drawing selected charged particles from the particle mixture, a collector, and a transition outlet. The length of the electric field zone is selectively adjustable for varying the charged particle drawing action. A curtain gas flow introduced into the separation chamber carries the selected charged particles drawn from the particle mixture in the electric field zone to the collector. Flow vanes or straighteners are provided for both the particle mixture flow and the curtain gas flow to reduce turbulence in the separation chamber and improve separation efficiency. The collector includes a discharge outlet for discharging the selected charged particles to a first collection bin. The transition outlet receives the remaining particle flow and delivers it to a second collection bin for recovery or to a second separator to collect any remaining selected charged particles. The transition outlet reforms the flow to create turbulence to further tribocharge the particles prior to entering the second separator. A related method of particle separation is also disclosed

Particle Separation System Using Parallel Multistage Electrostatic Separators

An electrostatic separation apparatus includes of a plurality of separators is provided for separating a particle mixture into two constituent species. Each separator includes one and preferably a plurality of modular separation stages. Each stage of the separator includes a pair of separation subchambers each having an electric field zone for drawing selected charged particles from the particle mixture. A curtain gas flow is provided for each subchamber to entrain and carry the selected charged particles drawn from the particle mixture in the electric field zone to a collector associated with each subchamber for recovery. The inlets for the particle mixture and curtain gas flows are adapted to straighten and smooth the respective flows to reduce turbulence in the separation subchambers and improve separation efficiency. The particle flow remaining after the first separation stage passes through an outlet to a second stage, a recycle line, or if further separation is deemed unnecessary, to a collection device for recovery. The apparatus may include a plurality of single or multistage separators arranged in parallel such that simultaneous operation is possible. Further, the apparatus may be included as part of an overall separation system

Diagnostic Performance of convolutional neural networks for dental sexual dimorphism

Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.</p

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.</p

Electrochemically Reconfigurable Architected Materials

Architected materials can actively respond to external stimuli—such as mechanical forces, hydration and magnetic fields—by changing their geometries and thereby achieve novel functionalities. Such transformations are usually binary and volatile because they toggle between ‘on’ and ‘off’ states and require persistent external stimuli. Here we develop three-dimensional silicon-coated tetragonal microlattices that transform into sinusoidal patterns via cooperative beam buckling in response to an electrochemically driven silicon-lithium alloying reaction. In situ microscopy reveals a controllable, non-volatile and reversible structural transformation that forms multiple ordered buckling domains separated by distorted domain boundaries. We investigate the mechanical dynamics of individual buckling beams, cooperative coupling among neighbouring beams, and lithiation-rate-dependent distributions of domain sizes through chemo-mechanical modelling and statistical mechanics analysis. Our results highlight the critical role of defects and energy fluctuations in the dynamic response of architected materials. We further demonstrate that domain boundaries can be programmed to form particular patterns by pre-designing artificial defects, and that a variety of reconfigurational degrees of freedom can be achieved through micro-architecture design. This framework enables the design, fabrication, modelling, behaviour prediction and programming of electrochemically reconfigurable architected materials, and could open the way to beyond-intercalation battery electrodes, tunable phononic crystals and bio-implantable devices

Neonatal outcome in 29 pregnant women with COVID-19 : A retrospective study in Wuhan, China

Funding: YTW: National Key Research and Development Program of China (2018YFC1002804), http://www.most.gov.cn; YTW: National Key Research and Development Program of China (2016YFC1000203), http://www.most.gov.cn. CL: COVID-19 Prevention and Control Program of International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University (2020-COVID-19-04), https://www.ipmch.com.cn. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability: All the research data are available at the ResMen Manager of Chinese Clinical Trial Registry (www.medresman.org), and the registration number is ChiCTR2000031954 (http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=1810).Peer reviewedPublisher PD

Cancer-Derived Mutations in KEAP1 Impair NRF2 Degradation but not Ubiquitination

NRF2 is a transcription factor that mediates stress responses. Oncogenic mutations in NRF2 localize to one of its two binding interfaces with KEAP1, an E3 ubiquitin ligase that promotes proteasome-dependent degradation of NRF2. Somatic mutations in KEAP1 occur commonly in human cancer, where KEAP1 may function as a tumor suppressor. These mutations distribute throughout the KEAP1 protein but little is known about their functional impact. In this study, we characterized 18 KEAP1 mutations defined in a lung squamous cell carcinoma tumor set. Four mutations behaved as wild-type KEAP1, thus are likely passenger events. R554Q, W544C, N469fs, P318fs, and G333C mutations attenuated binding and suppression of NRF2 activity. The remaining mutations exhibited hypomorphic suppression of NRF2, binding both NRF2 and CUL3. Proteomic analysis revealed that the R320Q, R470C, G423V, D422N, G186R, S243C, and V155F mutations augmented the binding of KEAP1 and NRF2. Intriguingly, these 'super-binder' mutants exhibited reduced degradation of NRF2. Cell-based and in vitro biochemical analyses demonstrated that despite its inability to suppress NRF2 activity, the R320Q 'superbinder' mutant maintained the ability to ubiquitinate NRF2. These data strengthen the genetic interactions between KEAP1 and NRF2 in cancer and provide new insight into KEAP1 mechanics